Abstract
The Automated Rock melon Grading Technology presents a pioneering solution set to transform the agricultural landscape, particularly in the grading of rock melon. The system integrates state-of-the-art technologies such as weight sensors and high-resolution cameras to revolutionise the grading process by addressing the inherent challenges of manual grading methods prevalent in the industry. The system ensures rapid, precise, and consistent grading outcomes through a meticulously designed multi-stage approach, starting from weight measurement for quantitative assessment and culminating in visual inspection for quality attributes. This comprehensive methodology enhances accuracy and efficiency and minimises manual intervention, significantly improving productivity throughout the grading process. Moreover, the system's scalability and adaptability, facilitated by customisable options, cater to stakeholders' diverse scales and requirements across the agricultural supply chain, from farms to distributors. The technology provides real-time data insights and adaptive learning capabilities by leveraging sensors and machine learning (ML) algorithms, enabling continuous refinement and optimisation of grading processes. This technological innovation holds immense promise for advancing operational efficiency, quality control, and sustainability in rock melon production, thereby meeting the evolving demands of consumers and stakeholders while contributing to the overall advancement of the agricultural industry.
Metadata
Item Type: | Conference or Workshop Item (Paper) |
---|---|
Creators: | Creators Email / ID Num. Chua, Hong Kheng UNSPECIFIED Selvaraju, Haarinesh UNSPECIFIED Taufik, Intan Amani UNSPECIFIED Sahabudin, Muhammad Afiq UNSPECIFIED |
Contributors: | Contribution Name Email / ID Num. Chief Editor Abdul Rahman, Zarinatun Ilyani UNSPECIFIED Editor Mohd Nasir, Nur Fatima Wahida UNSPECIFIED Editor Kamarudin, Syaza UNSPECIFIED Designer Ramlie, Mohd Khairulnizam UNSPECIFIED |
Subjects: | T Technology > T Technology (General) > Technological change > Technological innovations |
Divisions: | Universiti Teknologi MARA, Perak > Seri Iskandar Campus > Faculty of Architecture, Planning and Surveying |
Journal or Publication Title: | The 13th International Innovation, Invention & Design Competition 2024 |
Page Range: | pp. 199-202 |
Keywords: | Automated, Machine Learning, Dataset, Grading, Fruit, Vegetable, Rock melon, AgTech |
Date: | 2024 |
URI: | https://ir.uitm.edu.my/id/eprint/105328 |